{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"mgmL2UGJys14"},"outputs":[],"source":["#|default_exp models.TST"]},{"cell_type":"markdown","metadata":{"id":"SlF873bHys1-"},"source":["# TST"]},{"cell_type":"markdown","metadata":{"id":"u_d4aVcRys2A"},"source":["This is an unofficial PyTorch implementation by Ignacio Oguiza of  - timeseriesAI@gmail.com based on:\n","* George Zerveas et al. A Transformer-based Framework for Multivariate Time Series Representation Learning, in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '21), August 14--18, 2021. ArXiV version: https://arxiv.org/abs/2010.02803\n","* Official implementation: https://github.com/gzerveas/mvts_transformer\n","\n","```bash\n","@inproceedings{10.1145/3447548.3467401,\n","author = {Zerveas, George and Jayaraman, Srideepika and Patel, Dhaval and Bhamidipaty, Anuradha and Eickhoff, Carsten},\n","title = {A Transformer-Based Framework for Multivariate Time Series Representation Learning},\n","year = {2021},\n","isbn = {9781450383325},\n","publisher = {Association for Computing Machinery},\n","address = {New York, NY, USA},\n","url = {https://doi.org/10.1145/3447548.3467401},\n","doi = {10.1145/3447548.3467401},\n","booktitle = {Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &amp; Data Mining},\n","pages = {2114–2124},\n","numpages = {11},\n","keywords = {regression, framework, multivariate time series, classification, transformer, deep learning, self-supervised learning, unsupervised learning, imputation},\n","location = {Virtual Event, Singapore},\n","series = {KDD '21}\n","}\n","```\n","\n","\n","This paper uses 'Attention is all you need' as a major reference:\n","* Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). **Attention is all you need**. In Advances in neural information processing systems (pp. 5998-6008).\n","\n","This implementation is adapted to work with the rest of the `tsai` library, and contain some hyperparameters that are not available in the original implementation. They are included to experiment with them. "]},{"cell_type":"markdown","metadata":{"id":"uyM1tPeTys2C"},"source":["## TST arguments"]},{"cell_type":"markdown","metadata":{"id":"lsxXfFGlys2D"},"source":["Usual values are the ones that appear in the \"Attention is all you need\" and \"A Transformer-based Framework for Multivariate Time Series Representation Learning\" papers. \n","\n","The default values are the ones selected as a default configuration in the latter.\n","\n","* c_in: the number of features (aka variables, dimensions, channels) in the time series dataset. dls.var\n","* c_out: the number of target classes. dls.c\n","* seq_len: number of time steps in the time series. dls.len\n","* max_seq_len: useful to control the temporal resolution in long time series to avoid memory issues. Default. None.\n","* d_model: total dimension of the model (number of features created by the model). Usual values: 128-1024. Default: 128.\n","* n_heads:  parallel attention heads. Usual values: 8-16. Default: 16.\n","* d_k: size of the learned linear projection of queries and keys in the MHA. Usual values: 16-512. Default: None -> (d_model/n_heads) = 32.\n","* d_v: size of the learned linear projection of values in the MHA. Usual values: 16-512. Default: None -> (d_model/n_heads) = 32.\n","* d_ff: the dimension of the feedforward network model. Usual values: 256-4096. Default: 256.\n","* dropout: amount of residual dropout applied in the encoder. Usual values: 0.-0.3. Default: 0.1.\n","* activation: the activation function of intermediate layer, relu or gelu. Default: 'gelu'.\n","* n_layers: the number of sub-encoder-layers in the encoder. Usual values: 2-8. Default: 3.\n","* fc_dropout: dropout applied to the final fully connected layer. Usual values: 0.-0.8. Default: 0.\n","* y_range: range of possible y values (used in regression tasks). Default: None\n","* kwargs: nn.Conv1d kwargs. If not {}, a nn.Conv1d with those kwargs will be applied to original time series."]},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":44746,"status":"ok","timestamp":1672964579800,"user":{"displayName":"Mark Karlov","userId":"14831590874336884869"},"user_tz":300},"id":"WJu1YTCez7FB","outputId":"db896d4b-5ef6-4f94-bc4e-cc74bd6cc475"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/gdrive\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting ecg_plot\n","  Downloading ecg_plot-0.2.8-py3-none-any.whl (9.2 kB)\n","Installing collected packages: ecg_plot\n","Successfully installed ecg_plot-0.2.8\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting pc\n","  Downloading pc-0.0.3.tar.gz (5.7 kB)\n","  Preparing metadata (setup.py) ... 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nn\n","import torch.nn.functional as F\n","import torch.optim as optim\n","from torchvision import datasets,transforms\n","from torch.utils.data.sampler import SubsetRandomSampler\n","import matplotlib.pyplot as plt\n","from torch.utils.data import Dataset, DataLoader\n","from transformers import BertTokenizer, BertModel\n","\n","import math\n","from typing import Tuple\n","import copy\n","\n","import torch\n","from torch import nn, Tensor\n","import torch.nn.functional as F\n","from torch.nn import TransformerEncoder, TransformerEncoderLayer\n","from torch.utils.data import dataset\n","\n","import torchvision"]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8772,"status":"ok","timestamp":1672964591174,"user":{"displayName":"Mark Karlov","userId":"14831590874336884869"},"user_tz":300},"id":"LfGl69yA2uRW","outputId":"5e3b89c4-ad69-4ccb-a873-281e336b2851"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting tsai\n","  Downloading tsai-0.3.4-py3-none-any.whl (272 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m272.3/272.3 KB\u001b[0m \u001b[31m19.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: fastai>=2.7.9 in /usr/local/lib/python3.8/dist-packages (from tsai) (2.7.10)\n","Collecting pyts>=0.12.0\n","  Downloading pyts-0.12.0-py3-none-any.whl (2.5 MB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.5/2.5 MB\u001b[0m \u001b[31m34.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: psutil>=5.4.8 in 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tsai"]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":33248,"status":"ok","timestamp":1672964626011,"user":{"displayName":"Mark Karlov","userId":"14831590874336884869"},"user_tz":300},"id":"IWwq09vs0Bl2","outputId":"c483a2cd-a786-410e-ba9c-eae503a223b6"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","  inflating: ptb-xl-a-large-publicly-available-electrocardiography-dataset-1.0.2/records100/19000/19338_lr.hea  \n","  inflating: ptb-xl-a-large-publicly-available-electrocardiography-dataset-1.0.2/records100/19000/19339_lr.dat  \n","  inflating: ptb-xl-a-large-publicly-available-electrocardiography-dataset-1.0.2/records100/19000/19339_lr.hea  \n","  inflating: ptb-xl-a-large-publicly-available-electrocardiography-dataset-1.0.2/records100/19000/19340_lr.dat  \n","  inflating: 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\"/content/gdrive/MyDrive/CardiacAbnormalityTransformerProject/physionet.org/ptb-xl-a-large-publicly-available-electrocardiography-dataset-1.0.2.zip\""]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4298,"status":"ok","timestamp":1672964630305,"user":{"displayName":"Mark Karlov","userId":"14831590874336884869"},"user_tz":300},"id":"luzMxz7m30SD","outputId":"cae8d8a1-1226-471b-de51-e0231aa42ab5"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","  inflating: 6273.tensor             \n","  inflating: 6274.tensor             \n","  inflating: 6275.tensor             \n","  inflating: 6276.tensor             \n","  inflating: 6277.tensor             \n","  inflating: 6278.tensor             \n","  inflating: 6279.tensor             \n","  inflating: 6280.tensor             \n","  inflating: 6281.tensor             \n","  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       \n","  inflating: 11270.tensor            \n","unzip:  cannot find or open /content/gdrive/MyDrive/Colab Notebooks/CardiacAbnormalityTransformerProject/clinicalEmbeddings.zip, /content/gdrive/MyDrive/Colab Notebooks/CardiacAbnormalityTransformerProject/clinicalEmbeddings.zip.zip or /content/gdrive/MyDrive/Colab Notebooks/CardiacAbnormalityTransformerProject/clinicalEmbeddings.zip.ZIP.\n","unzip:  cannot find or open /content/gdrive/MyDrive/Fall 2022/APS360/CardiacAbnormalityTransformerProject/clinicalEmbeddings.zip, /content/gdrive/MyDrive/Fall 2022/APS360/CardiacAbnormalityTransformerProject/clinicalEmbeddings.zip.zip or /content/gdrive/MyDrive/Fall 2022/APS360/CardiacAbnormalityTransformerProject/clinicalEmbeddings.zip.ZIP.\n"]}],"source":["# Chris\n","!unzip \"/content/gdrive/MyDrive/CardiacAbnormalityTransformerProject/clinicalEmbeddings.zip\"\n","\n","# Ankur\n","!unzip \"/content/gdrive/MyDrive/Colab Notebooks/CardiacAbnormalityTransformerProject/clinicalEmbeddings.zip\"\n","\n","#Meghna\n","!unzip \"/content/gdrive/MyDrive/Fall 2022/APS360/CardiacAbnormalityTransformerProject/clinicalEmbeddings.zip\""]},{"cell_type":"code","execution_count":5,"metadata":{"executionInfo":{"elapsed":5,"status":"ok","timestamp":1672964630305,"user":{"displayName":"Mark Karlov","userId":"14831590874336884869"},"user_tz":300},"id":"qw_PxFbR0IZH"},"outputs":[],"source":["def load_raw_data(df, sampling_rate, path):\n","    if sampling_rate == 100:\n","        data = [wfdb.rdsamp(path+f)[0] for f in df.filename_lr]\n","    else:\n","        data = [wfdb.rdsamp(path+f) for f in df.filename_hr]\n","    data = np.array(data)\n","    return data"]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":29093,"status":"ok","timestamp":1672964659394,"user":{"displayName":"Mark Karlov","userId":"14831590874336884869"},"user_tz":300},"id":"jtpa6z910JfY","outputId":"39f9e8e4-eec9-4f7a-d234-81bfe6c5a4e0"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(11271, 1000, 12)"]},"metadata":{},"execution_count":6}],"source":["# Shortcut to preprocessing\n","# Load raw signal data\n","#df = pd.read_csv('/content/gdrive/MyDrive/Colab Notebooks/CardiacAbnormalityTransformerProject/undersampledY')\n","df = pd.read_csv('/content/gdrive/MyDrive/CardiacAbnormalityTransformerProject/undersampledY')\n","# Meghna's path\n","#df = pd.read_csv('/content/gdrive/MyDrive/Fall 2022/APS360/CardiacAbnormalityTransformerProject/undersampledY')\n","#df = df.head(20)\n","X = load_raw_data(df, 100, '/content/ptb-xl-a-large-publicly-available-electrocardiography-dataset-1.0.2/')\n","\n","# Check how many ecg segments\n","X.shape"]},{"cell_type":"code","execution_count":7,"metadata":{"executionInfo":{"elapsed":27,"status":"ok","timestamp":1672964659395,"user":{"displayName":"Mark Karlov","userId":"14831590874336884869"},"user_tz":300},"id":"ROW1hcHA0KrU"},"outputs":[],"source":["class M(Dataset):\n","    def __init__(self, diagnostics_superclass,X):\n","        self.labels = diagnostics_superclass\n","        self.data = X\n","        self.id = df.index.to_list()\n","        self.notes = df.report.to_list()\n","        self.map = {'[\\'NORM\\']':0,'[\\'MI\\']':1,'[\\'STTC\\']':2,'[\\'CD\\']':3,'[\\'HYP\\']':4, '[\\'HYP\\', \\'MI\\']':5, '[\\'HYP\\', \\'CD\\']':6,\n","                    '[\\'HYP\\', \\'STTC\\']':7,'[\\'MI\\', \\'CD\\']':8,'[\\'MI\\', \\'STTC\\']':9,'[\\'CD\\', \\'STTC\\']':10,'[\\'HYP\\', \\'MI\\', \\'STTC\\']':11,\n","                    '[\\'HYP\\', \\'MI\\', \\'CD\\']':12, '[\\'MI\\', \\'CD\\', \\'STTC\\']':13,'[\\'HYP\\', \\'CD\\', \\'STTC\\']':14,'[\\'HYP\\', \\'MI\\', \\'CD\\', \\'STTC\\']':15, \n","                    '[\\'CD\\', \\'NORM\\']':16, '[\\'CD\\', \\'NORM\\', \\'STTC\\']':17, '[\\'NORM\\', \\'STTC\\']':18, '[\\'HYP\\', \\'NORM\\']':19, '[\\'MI\\', \\'NORM\\']':20, \n","                    '[\\'HYP\\', \\'MI\\', \\'NORM\\']':21, '[\\'HYP\\', \\'CD\\', \\'NORM\\']':22, '[\\'HYP\\', \\'MI\\', \\'CD\\', \\'NORM\\']':23, '[\\'HYP\\', \\'MI\\', \\'CD\\', \\'NORM\\', \\'STTC\\']':24,\n","                    '[\\'HYP\\', \\'NORM\\', \\'STTC\\']':25, '[\\'MI\\', \\'NORM\\', \\'STTC\\']':26, '[\\'HYP\\', \\'MI\\', \\'NORM\\', \\'STTC\\']':27, '[\\'HYP\\', \\'CD\\', \\'NORM\\', \\'STTC\\']':28, '[\\'MI\\', \\'CD\\', \\'NORM\\', \\'STTC\\']':29}\n","    def __getitem__(self, i):\n","        return self.data[i][:250] , self.map[\"\".join(self.labels[i])], torch.load(\"/content/\"+ str(self.id[i]) + \".tensor\") \n","    def __len__(self):\n","        return len(self.labels)\n","diagnostics_superclass = df['diagnostic_superclass'].to_list()\n","diagnostics_superclass = np.array(diagnostics_superclass)\n","ds = M(diagnostics_superclass,X)\n","# dl = DataLoader(ds, batch_size=6)"]},{"cell_type":"code","execution_count":8,"metadata":{"executionInfo":{"elapsed":26,"status":"ok","timestamp":1672964659396,"user":{"displayName":"Mark Karlov","userId":"14831590874336884869"},"user_tz":300},"id":"TDlkQL4k0Mot"},"outputs":[],"source":["def get_data_loader(train_data,val_data,batch_size):\n","  #dataloader = torch.utils.data.DataLoader(dataset,batch_size=batch_size,shuffle=True)\n","  return torch.utils.data.DataLoader(train_data,batch_size=batch_size,shuffle=True), torch.utils.data.DataLoader(val_data,batch_size=batch_size,shuffle=True)\n","\n","def get_split_data(dataset):\n","  train_size = int(0.08 * len(dataset))\n","  valid_size = int(0.01 * len(dataset))\n","  test_size = len(dataset) - train_size - valid_size\n","  train_loader, val_loader, test_loader = torch.utils.data.random_split(dataset, [train_size, valid_size, test_size])\n","  return train_loader, val_loader, test_loader"]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":27,"status":"ok","timestamp":1672964659397,"user":{"displayName":"Mark Karlov","userId":"14831590874336884869"},"user_tz":300},"id":"ZjNqZyOj0Njg","outputId":"44763f8a-37c4-45a4-8747-1ff7f2526971"},"outputs":[{"output_type":"stream","name":"stdout","text":["901\n"]}],"source":["train_data, val_data, test_data = get_split_data(ds)\n","train_loader, val_loader = get_data_loader(train_data,val_data,1)\n","print(len(train_loader))"]},{"cell_type":"markdown","metadata":{"id":"mN6ZFLLSys2E"},"source":["## Imports"]},{"cell_type":"code","execution_count":10,"metadata":{"executionInfo":{"elapsed":1779,"status":"ok","timestamp":1672964664207,"user":{"displayName":"Mark Karlov","userId":"14831590874336884869"},"user_tz":300},"id":"T_Loh-ieys2F"},"outputs":[],"source":["#|export\n","from tsai.imports import *\n","from tsai.utils import *\n","from tsai.models.layers import *\n","from tsai.models.utils import *"]},{"cell_type":"markdown","metadata":{"id":"95VJKpQkys2G"},"source":["## TST"]},{"cell_type":"code","execution_count":11,"metadata":{"executionInfo":{"elapsed":124,"status":"ok","timestamp":1672964667073,"user":{"displayName":"Mark Karlov","userId":"14831590874336884869"},"user_tz":300},"id":"jJ_EmUVVys2H"},"outputs":[],"source":["#|exporti\n","class _ScaledDotProductAttention(Module):\n","    def __init__(self, d_k:int): self.d_k = d_k\n","    def forward(self, q:Tensor, k:Tensor, v:Tensor, mask:Optional[Tensor]=None):\n","\n","        # MatMul (q, k) - similarity scores for all pairs of positions in an input sequence\n","        scores = torch.matmul(q, k)                                         # scores : [bs x n_heads x q_len x q_len]\n","        \n","        # Scale\n","        scores = scores / (self.d_k ** 0.5)\n","        \n","        # Mask (optional)\n","        if mask is not None: scores.masked_fill_(mask, -1e9)\n","        \n","        # SoftMax\n","        attn = F.softmax(scores, dim=-1)                                    # attn   : [bs x n_heads x q_len x q_len]\n","        \n","        # MatMul (attn, v)\n","        context = torch.matmul(attn, v)                                     # context: [bs x n_heads x q_len x d_v]\n","        \n","        return context, attn"]},{"cell_type":"code","execution_count":12,"metadata":{"executionInfo":{"elapsed":119,"status":"ok","timestamp":1672964668200,"user":{"displayName":"Mark Karlov","userId":"14831590874336884869"},"user_tz":300},"id":"Qe9xQ8d7ys2K"},"outputs":[],"source":["#|exporti\n","class _MultiHeadAttention(Module):\n","    def __init__(self, d_model:int, n_heads:int, d_k:int, d_v:int):\n","        r\"\"\"\n","        Input shape:  Q, K, V:[batch_size (bs) x q_len x d_model], mask:[q_len x q_len]\n","        \"\"\"\n","        self.n_heads, self.d_k, self.d_v = n_heads, d_k, d_v\n","        \n","        self.W_Q = nn.Linear(d_model, d_k * n_heads, bias=False)\n","        self.W_K = nn.Linear(d_model, d_k * n_heads, bias=False)\n","        self.W_V = nn.Linear(d_model, d_v * n_heads, bias=False)\n","        \n","        self.W_O = nn.Linear(n_heads * d_v, d_model, bias=False)\n","\n","    def forward(self, Q:Tensor, K:Tensor, V:Tensor, mask:Optional[Tensor]=None):\n","        \n","        bs = Q.size(0)\n","\n","        # Linear (+ split in multiple heads)\n","        q_s = self.W_Q(Q).view(bs, -1, self.n_heads, self.d_k).transpose(1,2)       # q_s    : [bs x n_heads x q_len x d_k]\n","        k_s = self.W_K(K).view(bs, -1, self.n_heads, self.d_k).permute(0,2,3,1)     # k_s    : [bs x n_heads x d_k x q_len] - transpose(1,2) + transpose(2,3)\n","        v_s = self.W_V(V).view(bs, -1, self.n_heads, self.d_v).transpose(1,2)       # v_s    : [bs x n_heads x q_len x d_v]\n","\n","        # Scaled Dot-Product Attention (multiple heads)\n","        context, attn = _ScaledDotProductAttention(self.d_k)(q_s, k_s, v_s)          # context: [bs x n_heads x q_len x d_v], attn: [bs x n_heads x q_len x q_len]\n","\n","        # Concat\n","        context = context.transpose(1, 2).contiguous().view(bs, -1, self.n_heads * self.d_v) # context: [bs x q_len x n_heads * d_v]\n","\n","        # Linear\n","        output = self.W_O(context)                                                  # context: [bs x q_len x d_model]\n","        \n","        return output, attn"]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":260,"status":"ok","timestamp":1672964672293,"user":{"displayName":"Mark Karlov","userId":"14831590874336884869"},"user_tz":300},"id":"vu9S8OVOys2M","outputId":"94b017d5-1993-4b24-d50c-f3d22c5ea982"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(torch.Size([16, 50, 128]), torch.Size([16, 3, 50, 50]))"]},"metadata":{},"execution_count":13}],"source":["t = torch.rand(16, 50, 128)\n","output, attn = _MultiHeadAttention(d_model=128, n_heads=3, d_k=8, d_v=6)(t, t, t)\n","output.shape, attn.shape"]},{"cell_type":"code","execution_count":14,"metadata":{"executionInfo":{"elapsed":136,"status":"ok","timestamp":1672964672988,"user":{"displayName":"Mark Karlov","userId":"14831590874336884869"},"user_tz":300},"id":"ThFIpnjTys2O"},"outputs":[],"source":["#|exporti\n","def get_activation_fn(activation):\n","    if activation == \"relu\": return nn.ReLU()\n","    elif activation == \"gelu\": return nn.GELU()\n","    else: return activation()\n","#         raise ValueError(f'{activation} is not available. You can use \"relu\" or \"gelu\"')\n","\n","class _TSTEncoderLayer(Module):\n","    def __init__(self, q_len:int, d_model:int, n_heads:int, d_k:Optional[int]=None, d_v:Optional[int]=None, d_ff:int=256, dropout:float=0.1, \n","                 activation:str=\"gelu\"):\n","\n","        assert d_model // n_heads, f\"d_model ({d_model}) must be divisible by n_heads ({n_heads})\"\n","        d_k = ifnone(d_k, d_model // n_heads)\n","        d_v = ifnone(d_v, d_model // n_heads)\n","\n","        # Multi-Head attention\n","        self.self_attn = _MultiHeadAttention(d_model, n_heads, d_k, d_v)\n","\n","        # Add & Norm\n","        self.dropout_attn = nn.Dropout(dropout)\n","        self.batchnorm_attn = nn.Sequential(Transpose(1,2), nn.BatchNorm1d(d_model), Transpose(1,2))\n","\n","        # Position-wise Feed-Forward\n","        self.ff = nn.Sequential(nn.Linear(d_model, d_ff), \n","                                get_activation_fn(activation), \n","                                nn.Dropout(dropout), \n","                                nn.Linear(d_ff, d_model))\n","\n","        # Add & Norm\n","        self.dropout_ffn = nn.Dropout(dropout)\n","        self.batchnorm_ffn = nn.Sequential(Transpose(1,2), nn.BatchNorm1d(d_model), Transpose(1,2))\n","\n","    def forward(self, src:Tensor, mask:Optional[Tensor]=None) -> Tensor:\n","\n","        # Multi-Head attention sublayer\n","        ## Multi-Head attention\n","        src2, attn = self.self_attn(src, src, src, mask=mask)\n","        ## Add & Norm\n","        src = src + self.dropout_attn(src2) # Add: residual connection with residual dropout\n","        src = self.batchnorm_attn(src)      # Norm: batchnorm \n","\n","        # Feed-forward sublayer\n","        ## Position-wise Feed-Forward\n","        src2 = self.ff(src)\n","        ## Add & Norm\n","        src = src + self.dropout_ffn(src2) # Add: residual connection with residual dropout\n","        src = self.batchnorm_ffn(src) # Norm: batchnorm\n","\n","        return src"]},{"cell_type":"code","execution_count":15,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":214,"status":"ok","timestamp":1672964675049,"user":{"displayName":"Mark Karlov","userId":"14831590874336884869"},"user_tz":300},"id":"Nw7YpFkOys2P","outputId":"a825a885-5aa7-4ae8-83a3-fe859d3529ba"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([16, 50, 128])"]},"metadata":{},"execution_count":15}],"source":["t = torch.rand(16, 50, 128)\n","output = _TSTEncoderLayer(q_len=50, d_model=128, n_heads=3, d_k=None, d_v=None, d_ff=512, dropout=0.1, activation='gelu')(t)\n","output.shape"]},{"cell_type":"code","execution_count":16,"metadata":{"executionInfo":{"elapsed":121,"status":"ok","timestamp":1672964675889,"user":{"displayName":"Mark Karlov","userId":"14831590874336884869"},"user_tz":300},"id":"IE8gFucVys2P"},"outputs":[],"source":["#|exporti\n","class _TSTEncoder(Module):\n","    def __init__(self, q_len, d_model, n_heads, d_k=None, d_v=None, d_ff=None, dropout=0.1, activation='gelu', n_layers=1):\n","        \n","        self.layers = nn.ModuleList([_TSTEncoderLayer(q_len, d_model, n_heads=n_heads, d_k=d_k, d_v=d_v, d_ff=d_ff, dropout=dropout, \n","                                                            activation=activation) for i in range(n_layers)])\n","\n","    def forward(self, src):\n","        output = src\n","        for mod in self.layers: output = mod(output)\n","        return output"]},{"cell_type":"code","execution_count":59,"metadata":{"executionInfo":{"elapsed":129,"status":"ok","timestamp":1672966117266,"user":{"displayName":"Mark Karlov","userId":"14831590874336884869"},"user_tz":300},"id":"O-l-Ic_2ys2Q"},"outputs":[],"source":["#|export\n","class TST(Module):\n","    def __init__(self, c_in:int, c_out:int, seq_len:int, max_seq_len:Optional[int]=None, \n","                 n_layers:int=3, d_model:int=128, n_heads:int=16, d_k:Optional[int]=None, d_v:Optional[int]=None,  \n","                 d_ff:int=256, dropout:float=0.1, act:str=\"gelu\", fc_dropout:float=0., \n","                 y_range:Optional[tuple]=None, verbose:bool=False, **kwargs):\n","        r\"\"\"TST (Time Series Transformer) is a Transformer that takes continuous time series as inputs.\n","        As mentioned in the paper, the input must be standardized by_var based on the entire training set.\n","        Args:\n","            c_in: the number of features (aka variables, dimensions, channels) in the time series dataset.\n","            c_out: the number of target classes.\n","            seq_len: number of time steps in the time series.\n","            max_seq_len: useful to control the temporal resolution in long time series to avoid memory issues.\n","            d_model: total dimension of the model (number of features created by the model)\n","            n_heads:  parallel attention heads.\n","            d_k: size of the learned linear projection of queries and keys in the MHA. Usual values: 16-512. Default: None -> (d_model/n_heads) = 32.\n","            d_v: size of the learned linear projection of values in the MHA. Usual values: 16-512. Default: None -> (d_model/n_heads) = 32.\n","            d_ff: the dimension of the feedforward network model.\n","            dropout: amount of residual dropout applied in the encoder.\n","            act: the activation function of intermediate layer, relu or gelu.\n","            n_layers: the number of sub-encoder-layers in the encoder.\n","            fc_dropout: dropout applied to the final fully connected layer.\n","            y_range: range of possible y values (used in regression tasks).\n","            kwargs: nn.Conv1d kwargs. If not {}, a nn.Conv1d with those kwargs will be applied to original time series.\n","\n","        Input shape:\n","            bs (batch size) x nvars (aka features, variables, dimensions, channels) x seq_len (aka time steps)\n","        \"\"\"\n","        self.c_out, self.seq_len = c_out, seq_len\n","        \n","        # Input encoding\n","        q_len = seq_len\n","        self.new_q_len = False\n","        if max_seq_len is not None and seq_len > max_seq_len: # Control temporal resolution\n","            self.new_q_len = True\n","            q_len = max_seq_len\n","            tr_factor = math.ceil(seq_len / q_len)\n","            total_padding = (tr_factor * q_len - seq_len)\n","            padding = (total_padding // 2, total_padding - total_padding // 2)\n","            self.W_P = nn.Sequential(Pad1d(padding), Conv1d(c_in, d_model, kernel_size=tr_factor, padding=0, stride=tr_factor))\n","            pv(f'temporal resolution modified: {seq_len} --> {q_len} time steps: kernel_size={tr_factor}, stride={tr_factor}, padding={padding}.\\n', verbose)\n","        elif kwargs:\n","            self.new_q_len = True\n","            t = torch.rand(1, 1, seq_len)\n","            q_len = nn.Conv1d(1, 1, **kwargs)(t).shape[-1]\n","            self.W_P = nn.Conv1d(c_in, d_model, **kwargs) # Eq 2\n","            pv(f'Conv1d with kwargs={kwargs} applied to input to create input encodings\\n', verbose)\n","        else:\n","            self.W_P = nn.Linear(c_in, d_model) # Eq 1: projection of feature vectors onto a d-dim vector space\n","\n","        # Positional encoding\n","        W_pos = torch.empty((q_len, d_model), device=default_device())\n","        nn.init.uniform_(W_pos, -0.02, 0.02)\n","        self.W_pos = nn.Parameter(W_pos, requires_grad=True)\n","\n","        # Residual dropout\n","        self.dropout = nn.Dropout(dropout)\n","\n","        # Encoder\n","        self.encoder = _TSTEncoder(q_len, d_model, n_heads, d_k=d_k, d_v=d_v, d_ff=d_ff, dropout=dropout, activation=act, n_layers=n_layers)\n","        self.flatten = Flatten()\n","        \n","        # Head\n","        self.head_nf = q_len * d_model\n","        self.head = self.create_head(self.head_nf, c_out, act=act, fc_dropout=fc_dropout, y_range=y_range)\n","\n","    def create_head(self, nf, c_out, act=\"gelu\", fc_dropout=0., y_range=None, **kwargs):\n","        layers = [get_activation_fn(act), Flatten()]\n","        if fc_dropout: layers += [nn.Dropout(fc_dropout)]\n","        layers += [nn.Linear(nf, c_out)]\n","        if y_range: layers += [SigmoidRange(*y_range)]\n","        return nn.Sequential(*layers)    \n","        \n","\n","    def forward(self, x:Tensor, mask:Optional[Tensor]=None) -> Tensor:  # x: [bs x nvars x q_len]\n","\n","        # Input encoding\n","        if self.new_q_len: u = self.W_P(x).transpose(2,1) # Eq 2        # u: [bs x d_model x q_len] transposed to [bs x q_len x d_model]\n","        else: u = self.W_P(x.transpose(2,1)) # Eq 1                     # u: [bs x q_len x nvars] converted to [bs x q_len x d_model]\n","\n","        # Positional encoding\n","        u = self.dropout(u + self.W_pos)\n","\n","        # Encoder\n","        z = self.encoder(u)                                             # z: [bs x q_len x d_model]\n","        z = z.transpose(2,1).contiguous()                               # z: [bs x d_model x q_len]\n","\n","        # Classification/ Regression head\n","        return self.head(z)                                             # output: [bs x c_out]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ueJdBRWvys2R"},"outputs":[],"source":["# bs = 32\n","# c_in = 9  # aka channels, features, variables, dimensions\n","# c_out = 2\n","# seq_len = 5000\n","\n","# xb = torch.randn(bs, c_in, seq_len)\n","\n","# # standardize by channel by_var based on the training set\n","# xb = (xb - xb.mean((0, 2), keepdim=True)) / xb.std((0, 2), keepdim=True)\n","\n","# # Settings\n","# max_seq_len = 256\n","# d_model = 128\n","# n_heads = 16\n","# d_k = d_v = None # if None --> d_model // n_heads\n","# d_ff = 256\n","# dropout = 0.1\n","# activation = \"gelu\"\n","# n_layers = 3\n","# fc_dropout = 0.1\n","# kwargs = {}\n","\n","# model = TST(c_in, c_out, seq_len, max_seq_len=max_seq_len, d_model=d_model, n_heads=n_heads,\n","#             d_k=d_k, d_v=d_v, d_ff=d_ff, dropout=dropout, activation=activation, n_layers=n_layers,\n","#             fc_dropout=fc_dropout, **kwargs)\n","# test_eq(model.to(xb.device)(xb).shape, [bs, c_out])\n","# print(f'model parameters: {count_parameters(model)}')"]},{"cell_type":"code","execution_count":18,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":461},"executionInfo":{"elapsed":498,"status":"error","timestamp":1672964681364,"user":{"displayName":"Mark Karlov","userId":"14831590874336884869"},"user_tz":300},"id":"dO4YLikays2S","outputId":"96bd35fe-32b1-48ef-ae08-403784b885b5"},"outputs":[{"output_type":"error","ename":"ModuleNotFoundError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)","\u001b[0;32m<ipython-input-18-90c7404bc3e4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m#|hide\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtsai\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexport\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_nb_name\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mnb_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_nb_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtsai\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimports\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcreate_scripts\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mcreate_scripts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnb_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tsai/imports.py\u001b[0m in \u001b[0;36mcreate_scripts\u001b[0;34m(nb_name, max_elapsed, wait)\u001b[0m\n\u001b[1;32m    168\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcreate_scripts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnb_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_elapsed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m60\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m     \u001b[0;34m\"Function that saves a notebook, converts it to .py and checks it's been correctly converted\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 170\u001b[0;31m     \u001b[0;32mfrom\u001b[0m \u001b[0mnbdev\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexport\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnb_export\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    171\u001b[0m     \u001b[0msave_nb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnb_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnb_name\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'nbdev'","","\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"],"errorDetails":{"actions":[{"action":"open_url","actionText":"Open Examples","url":"/notebooks/snippets/importing_libraries.ipynb"}]}}],"source":["#|eval: false\n","#|hide\n","from tsai.export import get_nb_name; nb_name = get_nb_name(locals())\n","from tsai.imports import create_scripts; create_scripts(nb_name)"]},{"cell_type":"code","execution_count":56,"metadata":{"executionInfo":{"elapsed":133,"status":"ok","timestamp":1672966085686,"user":{"displayName":"Mark Karlov","userId":"14831590874336884869"},"user_tz":300},"id":"0eGafZLRys2T"},"outputs":[],"source":["#@title Train TST\n","def evaluate(net, loader, criterion, batch_size):\n","  accurate = 0\n","  total = 0\n","\n","  total_err = 0\n","  total_loss = 0\n","  total_epoch = 0\n","  enum = 1\n","  wavelet = 'db4'\n","  level = 5\n","  for images, labels, note in iter(loader):\n","    #############################################\n","    #To Enable GPU Usage\n","    coeffs = pywt.wavedec(images, wavelet, level=level, axis=1)\n","    denoised_coeffs = [pywt.threshold(data=c, mode='soft', value=0.1) for c in coeffs]\n","    images = pywt.waverec(denoised_coeffs, wavelet, axis=1)\n","\n","    images = torch.tensor(images).float()\n","    if use_cuda and torch.cuda.is_available():\n","      images = images.cuda()\n","      labels = labels.cuda()\n","      note = note.cuda()\n","    #############################################\n","    output = net(images.transpose(1,2),note)\n","      \n","    loss = criterion(output,W[labels].float())\n","\n","    values, indices = torch.max(output,dim=1)\n","    for i in range(labels.size(0)):\n","      if W[labels][i][indices[i]] == 0:\n","        total_err += 1\n","    total_loss += loss.item()\n","    total_epoch += len(labels)\n","    enum = enum + 1\n","  err = float(total_err) / total_epoch\n","  loss = float(total_loss) / (enum+1)\n","  return err, loss"]},{"cell_type":"code","execution_count":57,"metadata":{"executionInfo":{"elapsed":136,"status":"ok","timestamp":1672966086991,"user":{"displayName":"Mark Karlov","userId":"14831590874336884869"},"user_tz":300},"id":"pn6POAQF3L4M"},"outputs":[],"source":["def train_net(net,train_data,val_data,batch_size=64,learning_rate=0.01,num_epochs=30):\n","  train_loader, val_loader = get_data_loader(train_data,val_data,batch_size)\n","  # The loss function will be Binary Cross Entropy (BCE). In this case we\n","  # will use the BCEWithLogitsLoss which takes unnormalized output from\n","  # the neural network and scalar label.\n","  # Optimizer will be SGD with Momentum.\n","  criterion = nn.BCEWithLogitsLoss()\n","  optimizer = optim.SGD(net.parameters(), lr=learning_rate, momentum=0.9)\n","  # Set up some numpy arrays to store the training/test loss/erruracy\n","  train_err = np.zeros(num_epochs)\n","  train_loss = np.zeros(num_epochs)\n","  val_err = np.zeros(num_epochs)\n","  val_loss = np.zeros(num_epochs)\n","  wavelet = 'db4'\n","  level = 5\n","   # training\n","  epoch = 0 # the number of iterations\n","  for epoch in range(num_epochs):\n","    total_train_loss = 0.0\n","    total_train_err = 0.0\n","    total_epoch = 0\n","    enum = 0\n","    for images, labels, note in iter(train_loader):\n","      optimizer.zero_grad()\n","      #############################################\n","      #To Enable GPU Usage\n","      coeffs = pywt.wavedec(images, wavelet, level=level, axis=1)\n","      denoised_coeffs = [pywt.threshold(data=c, mode='soft', value=0.1) for c in coeffs]\n","      images = pywt.waverec(denoised_coeffs, wavelet, axis=1)\n"," \n","      images = torch.tensor(images).float()\n","      if use_cuda and torch.cuda.is_available():\n","        images = images.cuda()\n","        labels = labels.cuda()\n","        note = note.cuda()\n","      ############################################# \n","\n","      #output = net(torch.reshape(images,(batch_size,12,250)),note)\n","      output = net(images.transpose(1,2),note)\n","      loss = criterion(output,W[labels].float())\n","  \n","      loss.backward()\n","      optimizer.step()\n","\n","      values, indices = torch.max(output,dim=1)\n","      for i in range(labels.size(0)):\n","        if W[labels][i][indices[i]] == 0:\n","          total_train_err += 1\n","      total_train_loss += loss.item()\n","      total_epoch += len(labels)\n","      enum = enum + 1\n","    train_err[epoch] = float(total_train_err) / total_epoch\n","    train_loss[epoch] = float(total_train_loss) / (enum+1)\n","    val_err[epoch], val_loss[epoch] = evaluate(net, val_loader, criterion, batch_size)\n","    print((\"Epoch {}: Train Error: {}, Train loss: {} |\"+\n","               \"Validation Error: {}, Validation loss: {}\").format(\n","                   epoch + 1,\n","                   train_err[epoch],\n","                   train_loss[epoch],\n","                   val_err[epoch],\n","                   val_loss[epoch]))\n","  # plotting\n","  plt.title(\"Training Curve\")\n","  plt.plot(np.arange(1,num_epochs+1,1), train_loss, label=\"Train\")\n","  plt.plot(np.arange(1,num_epochs+1,1), val_loss, label=\"Validation\")\n","  plt.xlabel(\"Iterations\")\n","  plt.ylabel(\"Loss\")\n","  plt.legend(loc='best')\n","  plt.show()\n","\n","  plt.title(\"Training Curve\")\n","  plt.plot(np.arange(1,num_epochs+1,1), train_err, label=\"Train\")\n","  plt.plot(np.arange(1,num_epochs+1,1), val_err, label=\"Validation\")\n","  plt.xlabel(\"Iterations\")\n","  plt.ylabel(\"Training Error\")\n","  plt.legend(loc='best')\n","  plt.show()"]},{"cell_type":"code","execution_count":60,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":289,"status":"ok","timestamp":1672966121002,"user":{"displayName":"Mark Karlov","userId":"14831590874336884869"},"user_tz":300},"id":"dK8EXXOZys2S","outputId":"34e4821b-b262-4e95-8dee-bf0570d5c3cb"},"outputs":[{"output_type":"stream","name":"stdout","text":["model parameters: 8418250\n"]}],"source":["bs = 4\n","c_in = 12  # aka channels, features, variables, dimensions\n","c_out = 5\n","seq_len = 250\n","\n","xb = torch.randn(bs, c_in, seq_len)\n","\n","# standardize by channel by_var based on the training set\n","xb = (xb - xb.mean((0, 2), keepdim=True)) / xb.std((0, 2), keepdim=True)\n","\n","# Settings\n","max_seq_len = 250\n","d_model = 64\n","n_heads = 12\n","d_k = d_v = None # if None --> d_model // n_heads\n","d_ff = 64\n","dropout = 0.2\n","act = \"gelu\"\n","n_layers = 4\n","fc_dropout = 0.2\n","kwargs = {}\n","# kwargs = dict(kernel_size=5, padding=2)\n","\n","model = TST(c_in, c_out, seq_len, max_seq_len=max_seq_len, d_model=d_model, n_heads=n_heads,\n","            d_k=d_k, d_v=d_v, d_ff=d_ff, dropout=dropout, act=act, n_layers=n_layers,\n","            fc_dropout=fc_dropout, **kwargs)\n","test_eq(model.to(xb.device)(xb).shape, [bs, c_out])\n","print(f'model parameters: {count_parameters(model)}')"]},{"cell_type":"code","source":["import pywt"],"metadata":{"id":"jYqooOfafess","executionInfo":{"status":"ok","timestamp":1672965172037,"user_tz":300,"elapsed":3,"user":{"displayName":"Mark Karlov","userId":"14831590874336884869"}}},"execution_count":44,"outputs":[]},{"cell_type":"code","execution_count":45,"metadata":{"executionInfo":{"elapsed":2,"status":"ok","timestamp":1672965172862,"user":{"displayName":"Mark Karlov","userId":"14831590874336884869"},"user_tz":300},"id":"jS30oRo245kp"},"outputs":[],"source":["use_cuda = True\n","#{['NORM']:0,['MI']:1,['STTC']:2,['CD']:3,['HYP']:4, ['HYP','MI']:5, ['HYP','CD']:6,\n"," #                   ['HYP','STTC']:7,['MI','CD']:8,['MI','STTC']:9,['CD','STTC']:10,['HYP','MI','STTC']:11,\n","  #                  ['HYP','MI','CD']:12, ['HYP','MI','CD','STTC']:13}\n","W = np.array([[1,0,0,0,0],[0,1,0,0,0],[0,0,1,0,0],[0,0,0,1,0],[0,0,0,0,1],[0,1,0,0,1],[0,0,0,1,1],[0,0,1,0,1],[0,1,0,1,0],\n","              [0,1,1,0,0],[0,0,1,1,0],[0,1,1,0,1],[0,1,0,1,1],[0,1,1,1,0],[0,0,1,1,1],[0,1,1,1,1],[1,0,0,1,0],[1,0,1,1,0],[1,0,1,0,0],\n","              [1,0,0,0,1],[1,1,0,0,0],[1,1,0,0,1],[1,0,0,1,1],[1,1,0,1,1],[1,1,1,1,1],[1,0,1,0,1],[1,1,1,0,0],[1,1,1,0,1],[1,0,1,1,1],[1,1,1,1,0]])\n","W = torch.tensor(W)\n","if use_cuda and torch.cuda.is_available():\n","  W = W.cuda()"]},{"cell_type":"code","source":["model.head"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"X4XW5YfZiPcr","executionInfo":{"status":"ok","timestamp":1672965849869,"user_tz":300,"elapsed":175,"user":{"displayName":"Mark Karlov","userId":"14831590874336884869"}},"outputId":"32a94b6b-5a44-4eae-d5be-423d25a180f8"},"execution_count":54,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Sequential(\n","  (0): GELU(approximate='none')\n","  (1): fastai.layers.Flatten(full=False)\n","  (2): Dropout(p=0.2, inplace=False)\n","  (3): Linear(in_features=16000, out_features=5, bias=True)\n",")"]},"metadata":{},"execution_count":54}]},{"cell_type":"code","execution_count":61,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":850},"id":"vmQ9o7hT3Wex","executionInfo":{"status":"error","timestamp":1672966342014,"user_tz":300,"elapsed":211149,"user":{"displayName":"Mark Karlov","userId":"14831590874336884869"}},"outputId":"b7703287-8156-4d4b-8005-a0a1f65b87e1"},"outputs":[{"output_type":"stream","name":"stdout","text":["CUDA is available!  Training on GPU ...\n","Epoch 1: Train Error: 0.660377358490566, Train loss: 0.6835143171844229 |Validation Error: 0.6785714285714286, Validation loss: 0.6511042909375553\n","Epoch 2: Train Error: 0.6448390677025527, Train loss: 0.6641221145349266 |Validation Error: 0.6339285714285714, Validation loss: 0.6314605680005304\n","Epoch 3: Train Error: 0.6293007769145395, Train loss: 0.6471123232514457 |Validation Error: 0.6339285714285714, Validation loss: 0.6134859395438227\n","Epoch 4: Train Error: 0.607103218645949, Train loss: 0.6293629577993292 |Validation Error: 0.625, Validation loss: 0.5969511917952833\n","Epoch 5: Train Error: 0.5926748057713651, Train loss: 0.6131278008750055 |Validation Error: 0.6696428571428571, Validation loss: 0.5847113615479963\n","Epoch 6: Train Error: 0.5915649278579356, Train loss: 0.6030370023398273 |Validation Error: 0.5982142857142857, Validation loss: 0.57050896513051\n","Epoch 7: Train Error: 0.5693673695893452, Train loss: 0.5945927736769735 |Validation Error: 0.6428571428571429, Validation loss: 0.5690534690330769\n","Epoch 8: Train Error: 0.5360710321864595, Train loss: 0.5891794248492317 |Validation Error: 0.5982142857142857, Validation loss: 0.5655787597442495\n","Epoch 9: Train Error: 0.537180910099889, Train loss: 0.5860876252429675 |Validation Error: 0.5892857142857143, Validation loss: 0.5631270239065433\n","Epoch 10: Train Error: 0.5049944506104328, Train loss: 0.5795908843257785 |Validation Error: 0.5714285714285714, Validation loss: 0.5613645834141764\n","Epoch 11: Train Error: 0.5049944506104328, Train loss: 0.5762294385812979 |Validation Error: 0.6160714285714286, Validation loss: 0.5592664469932688\n","Epoch 12: Train Error: 0.46614872364039955, Train loss: 0.5710082324468984 |Validation Error: 0.5892857142857143, Validation loss: 0.5588782424556797\n","Epoch 13: Train Error: 0.462819089900111, Train loss: 0.5666574062367457 |Validation Error: 0.5803571428571429, Validation loss: 0.5609792460655344\n","Epoch 14: Train Error: 0.4439511653718091, Train loss: 0.5611573017276494 |Validation Error: 0.5714285714285714, Validation loss: 0.5616666628368969\n","Epoch 15: Train Error: 0.4328523862375139, Train loss: 0.5560844290704854 |Validation Error: 0.5446428571428571, Validation loss: 0.5597978126386116\n","Epoch 16: Train Error: 0.41287458379578246, Train loss: 0.5496550357974737 |Validation Error: 0.6071428571428571, Validation loss: 0.5600548032028921\n","Epoch 17: Train Error: 0.40732519422863483, Train loss: 0.5429192536172613 |Validation Error: 0.5803571428571429, Validation loss: 0.5630969451419239\n","Epoch 18: Train Error: 0.3762486126526082, Train loss: 0.5354282907416336 |Validation Error: 0.5446428571428571, Validation loss: 0.5603440157298384\n","Epoch 19: Train Error: 0.3751387347391787, Train loss: 0.5289570407002373 |Validation Error: 0.5535714285714286, Validation loss: 0.5626673174315485\n","Epoch 20: Train Error: 0.35738068812430634, Train loss: 0.5212092343552978 |Validation Error: 0.5446428571428571, Validation loss: 0.5621813181145437\n","Epoch 21: Train Error: 0.35405105438401774, Train loss: 0.5100654529786743 |Validation Error: 0.5982142857142857, Validation loss: 0.5596986582566952\n","Epoch 22: Train Error: 0.3362930077691454, Train loss: 0.5051506432679902 |Validation Error: 0.5535714285714286, Validation loss: 0.5659579018066669\n","Epoch 23: Train Error: 0.31076581576026635, Train loss: 0.4927287083613662 |Validation Error: 0.5892857142857143, Validation loss: 0.5615070025468695\n","Epoch 24: Train Error: 0.30965593784683687, Train loss: 0.48296765149035287 |Validation Error: 0.5803571428571429, Validation loss: 0.5682015326516382\n","Epoch 25: Train Error: 0.290788013318535, Train loss: 0.4758416420194955 |Validation Error: 0.5625, Validation loss: 0.570482451854081\n","Epoch 26: Train Error: 0.28190899001109876, Train loss: 0.4620702702519113 |Validation Error: 0.5982142857142857, Validation loss: 0.5692140431753521\n","Epoch 27: Train Error: 0.27968923418423974, Train loss: 0.4534781322344742 |Validation Error: 0.5803571428571429, Validation loss: 0.57801822238955\n","Epoch 28: Train Error: 0.2641509433962264, Train loss: 0.4420417451291485 |Validation Error: 0.5803571428571429, Validation loss: 0.5843169653210146\n"]},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-61-7e45edb9f801>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'CUDA is not available.  Training on CPU ...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mtrain_net\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mval_data\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlearning_rate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.0001\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnum_epochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m<ipython-input-57-f717b99c705d>\u001b[0m in \u001b[0;36mtrain_net\u001b[0;34m(net, train_data, val_data, batch_size, learning_rate, num_epochs)\u001b[0m\n\u001b[1;32m     40\u001b[0m       \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mW\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m       \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     43\u001b[0m       \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    485\u001b[0m                 \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    486\u001b[0m             )\n\u001b[0;32m--> 487\u001b[0;31m         torch.autograd.backward(\n\u001b[0m\u001b[1;32m    488\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    489\u001b[0m         )\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    195\u001b[0m     \u001b[0;31m# some Python versions print out the first line of a multi-line function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m     \u001b[0;31m# calls in the traceback and some print out the last line\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 197\u001b[0;31m     Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n\u001b[0m\u001b[1;32m    198\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m         allow_unreachable=True, accumulate_grad=True)  # Calls into the C++ engine to run the backward pass\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}],"source":["#@title Instantiate & Test\n","use_cuda = True\n","if use_cuda and torch.cuda.is_available():\n","  model.cuda()\n","  print('CUDA is available!  Training on GPU ...')\n","else:\n","  print('CUDA is not available.  Training on CPU ...')\n","\n","train_net(model,train_data,val_data,batch_size=2,learning_rate=0.0001,num_epochs=30)"]},{"cell_type":"code","source":["model"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"L5vGJuMxe7v8","executionInfo":{"status":"ok","timestamp":1672964982315,"user_tz":300,"elapsed":5,"user":{"displayName":"Mark Karlov","userId":"14831590874336884869"}},"outputId":"a456a650-9160-4d08-cd92-021fce78728b"},"execution_count":36,"outputs":[{"output_type":"execute_result","data":{"text/plain":["TST(\n","  (W_P): Linear(in_features=12, out_features=128, bias=True)\n","  (dropout): Dropout(p=0.1, inplace=False)\n","  (encoder): _TSTEncoder(\n","    (layers): ModuleList(\n","      (0): _TSTEncoderLayer(\n","        (self_attn): _MultiHeadAttention(\n","          (W_Q): Linear(in_features=128, out_features=120, bias=False)\n","          (W_K): Linear(in_features=128, out_features=120, bias=False)\n","          (W_V): Linear(in_features=128, out_features=120, bias=False)\n","          (W_O): Linear(in_features=120, out_features=128, bias=False)\n","        )\n","        (dropout_attn): Dropout(p=0.1, inplace=False)\n","        (batchnorm_attn): Sequential(\n","          (0): Transpose(1, 2)\n","          (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): Transpose(1, 2)\n","        )\n","        (ff): Sequential(\n","          (0): Linear(in_features=128, out_features=128, bias=True)\n","          (1): GELU(approximate='none')\n","          (2): Dropout(p=0.1, inplace=False)\n","          (3): Linear(in_features=128, out_features=128, bias=True)\n","        )\n","        (dropout_ffn): Dropout(p=0.1, inplace=False)\n","        (batchnorm_ffn): Sequential(\n","          (0): Transpose(1, 2)\n","          (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): Transpose(1, 2)\n","        )\n","      )\n","      (1): _TSTEncoderLayer(\n","        (self_attn): _MultiHeadAttention(\n","          (W_Q): Linear(in_features=128, out_features=120, bias=False)\n","          (W_K): Linear(in_features=128, out_features=120, bias=False)\n","          (W_V): Linear(in_features=128, out_features=120, bias=False)\n","          (W_O): Linear(in_features=120, out_features=128, bias=False)\n","        )\n","        (dropout_attn): Dropout(p=0.1, inplace=False)\n","        (batchnorm_attn): Sequential(\n","          (0): Transpose(1, 2)\n","          (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): Transpose(1, 2)\n","        )\n","        (ff): Sequential(\n","          (0): Linear(in_features=128, out_features=128, bias=True)\n","          (1): GELU(approximate='none')\n","          (2): Dropout(p=0.1, inplace=False)\n","          (3): Linear(in_features=128, out_features=128, bias=True)\n","        )\n","        (dropout_ffn): Dropout(p=0.1, inplace=False)\n","        (batchnorm_ffn): Sequential(\n","          (0): Transpose(1, 2)\n","          (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): Transpose(1, 2)\n","        )\n","      )\n","      (2): _TSTEncoderLayer(\n","        (self_attn): _MultiHeadAttention(\n","          (W_Q): Linear(in_features=128, out_features=120, bias=False)\n","          (W_K): Linear(in_features=128, out_features=120, bias=False)\n","          (W_V): Linear(in_features=128, out_features=120, bias=False)\n","          (W_O): Linear(in_features=120, out_features=128, bias=False)\n","        )\n","        (dropout_attn): Dropout(p=0.1, inplace=False)\n","        (batchnorm_attn): Sequential(\n","          (0): Transpose(1, 2)\n","          (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): Transpose(1, 2)\n","        )\n","        (ff): Sequential(\n","          (0): Linear(in_features=128, out_features=128, bias=True)\n","          (1): GELU(approximate='none')\n","          (2): Dropout(p=0.1, inplace=False)\n","          (3): Linear(in_features=128, out_features=128, bias=True)\n","        )\n","        (dropout_ffn): Dropout(p=0.1, inplace=False)\n","        (batchnorm_ffn): Sequential(\n","          (0): Transpose(1, 2)\n","          (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): Transpose(1, 2)\n","        )\n","      )\n","      (3): _TSTEncoderLayer(\n","        (self_attn): _MultiHeadAttention(\n","          (W_Q): Linear(in_features=128, out_features=120, bias=False)\n","          (W_K): Linear(in_features=128, out_features=120, bias=False)\n","          (W_V): Linear(in_features=128, out_features=120, bias=False)\n","          (W_O): Linear(in_features=120, out_features=128, bias=False)\n","        )\n","        (dropout_attn): Dropout(p=0.1, inplace=False)\n","        (batchnorm_attn): Sequential(\n","          (0): Transpose(1, 2)\n","          (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): Transpose(1, 2)\n","        )\n","        (ff): Sequential(\n","          (0): Linear(in_features=128, out_features=128, bias=True)\n","          (1): GELU(approximate='none')\n","          (2): Dropout(p=0.1, inplace=False)\n","          (3): Linear(in_features=128, out_features=128, bias=True)\n","        )\n","        (dropout_ffn): Dropout(p=0.1, inplace=False)\n","        (batchnorm_ffn): Sequential(\n","          (0): Transpose(1, 2)\n","          (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): Transpose(1, 2)\n","        )\n","      )\n","    )\n","  )\n","  (flatten): fastai.layers.Flatten(full=False)\n","  (head): Sequential(\n","    (0): GELU(approximate='none')\n","    (1): fastai.layers.Flatten(full=False)\n","    (2): Dropout(p=0.2, inplace=False)\n","    (3): Linear(in_features=32000, out_features=5, bias=True)\n","  )\n",")"]},"metadata":{},"execution_count":36}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"uyjUmSXq4WDE"},"outputs":[],"source":[]}],"metadata":{"accelerator":"GPU","colab":{"provenance":[]},"gpuClass":"standard","kernelspec":{"display_name":"Python 3.7.11 ('py37torch110')","language":"python","name":"python3"}},"nbformat":4,"nbformat_minor":0}